Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Researchers: Yuyin Zhou, Zhe Li, Song Bai, Chong Wang, Xinlei Chen, Mei Han, Elliot Fishman, Alan Yuille
Paper: http://ow.ly/IdmR50qiURd
#technology #artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning

✴️ @AI_Python_EN

CS294-158 Deep Unsupervised Learning

Ilya Sutskever guest lecture on GPT-2: https://lnkd.in/eNUSMTY

#DeepLearning #MachineLearning #UnsupervisedLearning

✴️ @AI_Python_EN

Big but Imperceptible Adversarial Perturbations via Semantic Manipulation
Researchers: Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, David A. Forsyth
Paper: http://ow.ly/1aDn50qiU7G
#machinelearning #artificialintelligence #bigdata #deeplearning

✴️ @AI_Python_EN

My reflection for today: it is okay to dream, but it is more important to focus on the present and polish the current skill even if you think the skill is irrelevant. For example, when I was at school, I was a statistics TA who did not like statistics, because I wanted to be an engineer. Statistics department was kind enough to give me a job because the engineering department did not have open positions at the time. Then I was hired as a Data Scientist, but I liked the reservoir simulation better because I dreamt to be an engineer, which led to my lay off. Then I wanted to be a Data Scientist, but I was a Spotfire Engineer. Again, this Data Science passion did not work out well with my Spotfire Engineer job. Now I think if I would focus on all current skills at the time, I would become a Data Scientist anyways and would have better-polished skills since data visualization and statistics are both needed in this job.
So the moral of the story is: excel at your current job and use it as a foundation for your dream. Do your job well. And learn things even though they seem irrelevant at the time - you never know what future holds - it turns out you will need them. While dreaming about the future, stay grounded and in the present. Every single opportunity is a gift.

✴️ @AI_Python_EN

Transition guide from Excel’s analyst to Python Programming for Data Analysis

1. From Excel to Pandas https://lnkd.in/fnU5apw
2. Communication & Data Storytelling https://lnkd.in/eqf5gUV
3. Data Manipulation with Python https://lnkd.in/g4DFNpJ
4. Data Visualization with Python (Matplotlib/Seaborn): https://lnkd.in/g_3fx_6
5. Advanced Pandas https://lnkd.in/fZWGp9B
6. Tricks on Pandas by Real Python https://lnkd.in/fXc9XSp
7. Becoming Efficient with Pandas https://lnkd.in/f64hU-Y
8. Pandas Advances Tips https://lnkd.in/fGyBc4c
9. Jupyter Notebook (Beginner) https://lnkd.in/fTFinFi
10. Jupyter Notebook (Advanced) https://lnkd.in/fFufePv

#datavisualization #python #programming #pydata #datasets #pandas #datasets

✴️ @AI_Python_EN

Liveness Detection with OpenCV - PyImageSearch
http://bit.ly/2VI91j6 #AI #DataScience #MachineLearning #DataScience

✴️ @AI_Python_EN

Stack Deep Learning Bootcamp

(Most of) Lectures of Day 1: https://lnkd.in/eei67vp

Happy learning!

#ArtificialIntelligence #DeepLearning #MachineLearning

✴️ @AI_Python_EN

Whether you’re a:
- data scientist
- data analyst
- data engineer
- statistician
- BI Specialist
- business analyst
- software engineer
- research scientist
- machine learning engineer

At the end of the day, you’re a problem solver.

#datascience #machinelearning #analytics

✴️ @AI_Python_EN

#machinelearning

✴️ @AI_Python_EN

Numerical Linear Algebra course is wonderful. https://github.com/fastai/numerical-linear-algebra …

Automated theorem prover driven by deep reinforcement learning: DeepHOL. Comes with a benchmark suite of 29,462 theorems to be proven. It can already prove 58% of them using 41"tactics".

PDF: https://arxiv.org/pdf/1904.03241.pdf

✴️ @AI_Python_EN

I wanna be a data scientist, but… how!?

https://link.medium.com/CUDoPvMOEV
#DataScience #ArtificialIntelligence #MachineLearning #DeepLearning #DataScientist


✴️ @AI_Python_EN

A thread of research that I've been particularly excited about lately is the linearized training of neural networks and the Neural Tangent Kernel. To that end, we're releasing code - written in JAX - that we've been using for our research:
https://github.com/google/neural-tangents

✴️ @AI_Python_EN

Useful #Linux cheat sheet for #datascientists

✴️ @AI_Python_EN

A great GitHub repository with tutorials on getting started with #PyTorch and TorchText for #sentimentanalysis in #Jupyter Notebooks. What a great resource!

https://github.com/bentrevett/pytorch-sentiment-analysis

✴️ @AI_Python_EN

#Datascience needs to move beyond #research to actually make a real impact in the #AI economy.

Agree?

#DeepLearning #artificialintelligence #machinelearning

✴️ @AI_Python_EN

Here is a list of handy tools to keep in your #DataScience toolbox:

- - -
➤ Data Science Platform (All-in-one Packages & IDE)
Anaconda - https://lnkd.in/gWHY_ij

➤ Programming Languages (Python, R, and SQL)
Python Zero-to-Hero
https://lnkd.in/gEyZd5W
SQL for Data Science
https://lnkd.in/gjvgdhZ
(https://lnkd.in/fZxEF-g)

➤ Data Science Libraries
Top 15 Python Libraries (SciKit-Learn, TensorFlow, NLTK, matplotlib, etc..)
https://lnkd.in/gw_f3Ga

➤ Distributed Systems (Spark, Hadoop, Kafka)
Spark - https://lnkd.in/gC92A64
Hadoop - https://lnkd.in/gKuxgwx
Kafka - https://lnkd.in/gBB9Ja7

➤ Version Control (Git)
https://lnkd.in/g5sJj2H

➤ Reproducibility and Virtual Machines (Docker)
https://lnkd.in/gzYjuuA

➤ Cloud Services (AWS, Google Cloud, Microsoft Azure)
https://lnkd.in/gBJeQuY

➤ Serverless Architecture (Firebase)
https://lnkd.in/gbB6eeM

Data Warehouse and Data Lake
https://lnkd.in/gepNRMw


- - -
This list contains a high-level overview of the many tools out there that can be used for Data Science.

It's always great to refer back to your tools and keep things in check.

Hope this helps 🙂

✴️ @AI_Python_EN

The Full Stack #DeepLearning Bootcamp was a lot of fun in person, but of course not everyone can make it in person. Very excited to start releasing the materials today, here:

https://lnkd.in/giizppb

Happy learning from home!

✴️ @AI_Python_EN

Haha so funny! A typical day in the life of a machine learner 😂 #deeplearning #machinelearning #fun

✴️ @AI_Python_EN

Weakly Supervised Gaussian Networks for Action Detection
Researchers: Basura Fernando, Cheston Tan Yin Chet, Hakan Bilen
Paper: http://ow.ly/NC0S50qAP7S

#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning

✴️ @AI_Python_EN